SoroushVahidi/scidocs huggingface dataset:
An ontology for representing financial headline news
(2010)

This paper presents the development of an ontology to represent financial headline news. This ontology is developed using the On-To-Knowledge methodology where the focus is on the design steps of the Knowledge Meta Process. This development is part of an ongoing project which aims to design a virtual stock market simulator based on multi-agent systems. The proposed ontology has 31 concepts and includes 201 attributes. The testing results conducted on reliable headline news show that 99% of these headline news can be properly represented by the attributes of the right category in the ontology. Unreliable headline news characterized by news having uncertainties, incompleteness, ill-definition, or imprecision cannot be represented by the proposed ontology. Approaches for representing these unreliable headline
The real-time video stabilization for the rescue robot
(2009)

When rescue robots navigate in a rough terrain, significant vibration of the video occurs unavoidably and a video stabilization system is proposed in this paper to reduce the disturbance on the visual system of the present rescue robots. The Kalman filter is applied to estimate the motion vector of the robot and the false estimation occurrence can be thus greatly reduced by applying analysis of correlation and variance of the motion vector estimation. With a hierarchical searching algorithm implemented on the TI DSP 6437, the frame rate can be improved from 10 fps to 28 fps to realize the real-time video stabilization and furthermore, the cooperative mission of multiple rescue robots has been achieved by applying the developed visual servo technique. The proposed virtual bounded motion control algorithm further leads the robots approaching the target precisely with cooperation of multiple rescue robots. The demo video is available at: http://lab816.cn.nctu.edu.tw/DemoVideo/. video stabilization
Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units
(2016)

We propose the Gaussian Error Linear Unit (GELU), a high-performing neural network activation function. The GELU nonlinearity is the expected transformation of a stochastic regularizer which randomly applies the identity or zero map, combining the intuitions of dropout and zoneout while respecting neuron values. This connection suggests a new probabilistic understanding of nonlinearities. We perform an empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and find performance improvements across all tasks.
Device-to-Device Communication Underlaying a Finite Cellular Network Region
(2017)

Underlay in-band device-to-device (D2D) communication can improve the spectrum efficiency of cellular networks. However, the coexistence of D2D and cellular users causes inter-cell and intra-cell interference. The former can be effectively managed through inter-cell interference coordination and, therefore, is not considered in this paper. Instead, we focus on the intra-cell interference and propose a D2D mode selection scheme to manage it inside a finite cellular network region. The potential D2D users are controlled by the base station (BS) to operate in D2D mode based on the average interference generated to the BS. Using stochastic geometry, we study the outage probability experienced at the BS and a D2D receiver, and spectrum reuse ratio, which quantifies the average fraction of successfully transmitting D2D users. The analysis shows that the outage probability at the D2D receiver varies for different locations. In addition, without impairing the performance at the BS, if the path-loss exponent on the cellular link is slightly lower than that on the D2D link, the spectrum reuse ratio can have negligible decrease, while the D2D users’ average number of successful transmissions increases with increasing D2D node density. This indicates that an increasing level of D2D communication can be beneficial in future networks.
BLAT--the BLAST-like alignment tool.
(2002)

Analyzing vertebrate genomes requires rapid mRNA/DNA and cross-species protein alignments. A new tool, BLAT, is more accurate and 500 times faster than popular existing tools for mRNA/DNA alignments and 50 times faster for protein alignments at sensitivity settings typically used when comparing vertebrate sequences. BLAT's speed stems from an index of all nonoverlapping K-mers in the genome. This index fits inside the RAM of inexpensive computers, and need only be computed once for each genome assembly. BLAT has several major stages. It uses the index to find regions in the genome likely to be homologous to the query sequence. It performs an alignment between homologous regions. It stitches together these aligned regions (often exons) into larger alignments (typically genes). Finally, BLAT revisits small internal exons possibly missed at the first stage and adjusts large gap boundaries that have canonical splice sites where feasible. This paper describes how BLAT was optimized. Effects on speed and sensitivity are explored for various K-mer sizes, mismatch schemes, and number of required index matches. BLAT is compared with other alignment programs on various test sets and then used in several genome-wide applications. http://genome.ucsc.edu hosts a web-based BLAT server for the human genome.
The Web Never Forgets: Persistent Tracking Mechanisms in the Wild
(2014)

We present the first large-scale studies of three advanced web tracking mechanisms - canvas fingerprinting, evercookies and use of "cookie syncing" in conjunction with evercookies. Canvas fingerprinting, a recently developed form of browser fingerprinting, has not previously been reported in the wild; our results show that over 5% of the top 100,000 websites employ it. We then present the first automated study of evercookies and respawning and the discovery of a new evercookie vector, IndexedDB. Turning to cookie syncing, we present novel techniques for detection and analysing ID flows and we quantify the amplification of privacy-intrusive tracking practices due to cookie syncing.
 Our evaluation of the defensive techniques used by privacy-aware users finds that there exist subtle pitfalls --- such as failing to clear state on multiple browsers at once - in which a single lapse in judgement can shatter privacy defenses. This suggests that even sophisticated users face great difficulties in evading tracking techniques.
Detecting and Defending Against Third-Party Tracking on the Web
(2012)

While third-party tracking on the web has garnered much attention, its workings remain poorly understood. Our goal is to dissect how mainstream web tracking occurs in the wild. We develop a client-side method for detecting and classifying five kinds of third-party trackers based on how they manipulate browser state. We run our detection system while browsing the web and observe a rich ecosystem, with over 500 unique trackers in our measurements alone. We find that most commercial pages are tracked by multiple parties, trackers vary widely in their coverage with a small number being widely deployed, and many trackers exhibit a combination of tracking behaviors. Based on web search traces taken from AOL data, we estimate that several trackers can each capture more than 20% of a user’s browsing behavior. We further assess the impact of defenses on tracking and find that no existing browser mechanisms prevent tracking by social media sites via widgets while still allowing those widgets to achieve their utility goals, which leads us to develop a new defense. To the best of our knowledge, our work is the most complete study of web tracking to date.
Cookieless Monster: Exploring the Ecosystem of Web-Based Device Fingerprinting
(2013)

The web has become an essential part of our society and is currently the main medium of information delivery. Billions of users browse the web on a daily basis, and there are single websites that have reached over one billion user accounts. In this environment, the ability to track users and their online habits can be very lucrative for advertising companies, yet very intrusive for the privacy of users. In this paper, we examine how web-based device fingerprinting currently works on the Internet. By analyzing the code of three popular browser-fingerprinting code providers, we reveal the techniques that allow websites to track users without the need of client-side identifiers. Among these techniques, we show how current commercial fingerprinting approaches use questionable practices, such as the circumvention of HTTP proxies to discover a user's real IP address and the installation of intrusive browser plugins. At the same time, we show how fragile the browser ecosystem is against fingerprinting through the use of novel browser-identifying techniques. With so many different vendors involved in browser development, we demonstrate how one can use diversions in the browsers' implementation to distinguish successfully not only the browser-family, but also specific major and minor versions. Browser extensions that help users spoof the user-agent of their browsers are also evaluated. We show that current commercial approaches can bypass the extensions, and, in addition, take advantage of their shortcomings by using them as additional fingerprinting features.
Understanding Malvertising Through Ad-Injecting Browser Extensions
(2015)

Malvertising is a malicious activity that leverages advertising to distribute various forms of malware. Because advertising is the key revenue generator for numerous Internet companies, large ad networks, such as Google, Yahoo and Microsoft, invest a lot of effort to mitigate malicious ads from their ad networks. This drives adversaries to look for alternative methods to deploy malvertising. In this paper, we show that browser extensions that use ads as their monetization strategy often facilitate the deployment of malvertising. Moreover, while some extensions simply serve ads from ad networks that support malvertising, other extensions maliciously alter the content of visited webpages to force users into installing malware. To measure the extent of these behaviors we developed Expector, a system that automatically inspects and identifies browser extensions that inject ads, and then classifies these ads as malicious or benign based on their landing pages. Using Expector, we automatically inspected over 18,000 Chrome browser extensions. We found 292 extensions that inject ads, and detected 56 extensions that participate in malvertising using 16 different ad networks and with a total user base of 602,417.
Trends and Lessons from Three Years Fighting Malicious Extensions
(2015)

In this work we expose wide-spread efforts by criminals to abuse the Chrome Web Store as a platform for distributing malicious extensions. A central component of our study is the design and implementation of WebEval, the first system that broadly identifies malicious extensions with a concrete, measurable detection rate of 96.5%. Over the last three years we detected 9,523 malicious extensions: nearly 10% of every extension submitted to the store. Despite a short window of operation—we removed 50% of malware within 25 minutes of creation— a handful of under 100 extensions escaped immediate detection and infected over 50 million Chrome users. Our results highlight that the extension abuse ecosystem is drastically different from malicious binaries: miscreants profit from web traffic and user tracking rather than email spam or banking theft.
Study of Lightning Surge Overvoltages at Substations Due to Direct Lightning Strokes to Phase Conductors
(2010)

Accurate predictions of lightning surge overvoltages are essential to power equipment insulation design. Recent observations of lightning strokes to ultra-high-voltage designed transmission lines confirmed direct lightning strokes caused by shielding failure and found phenomena unexplainable by conventional shielding theories. However, there are few detailed studies of direct lightning surge overvoltages. This study assumed direct lightning stroke currents based on observational data and performs electromagnetic transient program analysis of the gas-insulated switchgear (GIS) and transformer overvoltages at substations to study the basic characteristics of overvoltages due to direct lightning strokes and evaluate lightning protection design. Consequently, the maximum GIS overvoltages were found during back-flashovers, and the locations of maximum overvoltages from direct lightning strokes and back-flashovers differ. Direct lightning strokes may be more severe than back-flashovers for transformers. This paper also studied the overvoltage generation mechanism and showed the relationship of the maximum voltage to lightning stroke current and transformer capacitance.
GISMO: a Graphical Interactive Student Monitoring Tool for Course Management Systems
(2004)

This paper presents GISMO, a graphical interactive student monitoring and tracking system tool that extracts tracking data from an on-line course maintained with a Course Management System, and generates graphical representations that can be explored by course instructors to examine various aspects of distance students. GISMO uses techniques from Information Visualisation to render into an appropriate graphical manner the complex multidimensional student tracking data provided by the Course Management System. GISMO aims to help instructors to become aware of what is happening in their classes and provide a better support to their learners.
Mapping learning and game mechanics for serious games analysis
(2015)

While there is a consensus on the instructional potential of Serious Games (SGs), there is still a lack of methodologies and tools not only for design but also to support analysis and assessment. Filling this gap is one of the main aims of the Games and Learning Alliance (GALA, www.galanoe.eu) European Network of Excellence on Serious Games (SGs), which has a focus upon pedagogy-driven SGs. This paper relies on the assumption that the fundamental aspect of SG design consists in the translation of learning goals/practices into mechanical element of game-play, serving to an instructional purpose beside that of play and fun. This paper proposes the Learning Mechanics-Game Mechanics (LM-GM) model, which supports SG analysis and design by allowing reflection on the various pedagogical and game elements in a SG. The LM-GM model includes a set of pre-defined game mechanics and pedagogical elements that we have abstracted from literature on game studies and learning theories. Designers and analysts can exploit these mechanics to draw the LM-GM map for a game, so as to identify and highlight its main pedagogical and entertainment features, and their interrelations. The tool may also be useful for teachers to evaluate the effectiveness of a given game and better understand how to implement it in educational settings. A case study is reported to illustrate the framework’s support in determining how game-play and pedagogy intertwine in a SG. Finally, the paper presents the results of two comparative user tests demonstrating the advantages of the proposed model with respect to a similar state-of-the-art framework.
Colyseus: A Distributed Architecture for Online Multiplayer Games
(2006)

This paper presents the design, implementation, and evaluation of Colyseus, a distributed architecture for interactive multiplayer games. Colyseus takes advantage of a game’s tolerance for weakly consistent state and predictable workload to meet the tight latency constraints of game-play and maintain scalable communication costs. In addition, it provides a rich distributed query interface and effective pre-fetching subsystem to help locate and replicate objects before they are accessed at a node. We have implemented Colyseus and modified Quake II, a popular first person shooter game, to use it. Our measurements of Quake II and our own Colyseus-based game with hundreds of players shows that Colyseus effectively distributes game traffic across the participating nodes, allowing Colyseus to support low-latency game-play for an order of magnitude more players than existing single server designs, with similar per-node bandwidth costs.
AskWorld: Budget-Sensitive Query Evaluation for Knowledge-on-Demand
(2015)

Recently, several Web-scale knowledge harvesting systems have been built, each of which is competent at extracting information from certain types of data (e.g., unstructured text, structured tables on the web, etc.). In order to determine the response to a new query posed to such systems (e.g., is sugar a healthy food?), it is useful to integrate opinions from multiple systems. If a response is desired within a specific time budget (e.g., in less than 2 seconds), then maybe only a subset of these resources can be queried. In this paper, we address the problem of knowledge integration for on-demand time-budgeted query answering. We propose a new method, AskWorld, which learns a policy that chooses which queries to send to which resources, by accommodating varying budget constraints that are available only at query (test) time. Through extensive experiments on real world datasets, we demonstrate AskWorld’s capability in selecting most informative resources to query within test-time constraints, resulting in improved performance compared to competitive baselines.
Model-free control
(2013)

Model-free control” and the corresponding “intelligent” PID controllers (iPIDs), which already had many successful concrete applications, are presented here for the first time in an unified manner, where the new advances are taken into account. The basics of model-free control is now employing some old functional analysis and some elementary differential algebra. The estimation techniques become quite straightforward via a recent online parameter identification approach. The importance of iPIs and especially of iPs is deduced from the presence of friction. The strange industrial ubiquity of classic PID’s and the great difficulty for tuning them in complex situations is deduced, via an elementary sampling, from their connections with iPIDs. Several numerical simulations are presented which include some infinite-dimensional systems. They demonstrate not only the power of our intelligent controllers but also the great simplicity for tuning them.
A Distributed Test System Architecture for Open-source IoT Software
(2015)

In this paper, we discuss challenges that are specific to testing of open IoT software systems. The analysis reveals gaps compared to wireless sensor networks as well as embedded software. We propose a testing framework which (a) supports continuous integration techniques, (b) allows for the integration of project contributors to volunteer hardware and software resources to the test system, and (c) can function as a permanent distributed plugtest for network interoperability testing. The focus of this paper lies in open-source IoT development but many aspects are also applicable to closed-source projects.
On Poisson Graphical Models
(2013)

Undirected graphical models, such as Gaussian graphical models, Ising, and multinomial/categorical graphical models, are widely used in a variety of applications for modeling distributions over a large number of variables. These standard instances, however, are ill-suited to modeling count data, which are increasingly ubiquitous in big-data settings such as genomic sequencing data, user-ratings data, spatial incidence data, climate studies, and site visits. Existing classes of Poisson graphical models, which arise as the joint distributions that correspond to Poisson distributed node-conditional distributions, have a major drawback: they can only model negative conditional dependencies for reasons of normalizability given its infinite domain. In this paper, our objective is to modify the Poisson graphical model distribution so that it can capture a rich dependence structure between count-valued variables. We begin by discussing two strategies for truncating the Poisson distribution and show that only one of these leads to a valid joint distribution. While this model can accommodate a wider range of conditional dependencies, some limitations still remain. To address this, we investigate two additional novel variants of the Poisson distribution and their corresponding joint graphical model distributions. Our three novel approaches provide classes of Poisson-like graphical models that can capture both positive and negative conditional dependencies between count-valued variables. One can learn the graph structure of our models via penalized neighborhood selection, and we demonstrate the performance of our methods by learning simulated networks as well as a network from microRNA-sequencing data.
Different voltage-dependent thresholds for inducing long-term depression and long-term potentiation in slices of rat visual cortex
(1990)

IN the hippocampus and neocortex, high-frequency (tetanic) stimu-lation of an afferent pathway leads to long-term potentiation (LTP) of synaptic transmission1–5. In the hippocampus it has recently been shown that long-term depression (LTD) of excitatory transmission can also be induced by certain combinations of synaptic activation6,7. In most hippocampal8 and all neocortical pathways4,9–11 studied so far, the induction of LTP requires the activation of JV-methyl-D-aspartate (NMDA) receptorgated conductances. Here we report that LTD can occur in neurons of slices of the rat visual cortex and that the same tetanic stimulation can induce either LTP or LTD depending on the level of depolarization of the postsynaptic neuron. By applying intracellular current injections or pharmacological disinhibition to modify the depolarizing response of the postsynaptic neuron to tetanic stimulation, we show that the mechanisms of induction of LTD and LTP are both postsynaptic. LTD is obtained if postsynaptic depolarization exceeds a critical level but remains below a threshold related to NMDA receptorgated conductances, whereas LTP is induced if this second threshold is reached.
Identifying adverse drug events from patient social media: A case study for diabetes
(2015)

Patient social media sites have emerged as major platforms for discussion of treatments and drug side effects, making them a promising source for listening to patients' voices in adverse drug event reporting. However, extracting patient reports from social media continues to be a challenge in health informatics research. In light of the need for more robust extraction methods, the authors developed a novel information extraction framework for identifying adverse drug events from patient social media. They also conducted a case study on a major diabetes patient social media platform to evaluate their framework's performance. Their approach achieves an f-measure of 86 percent in recognizing discussion of medical events and treatments, an f-measure of 69 percent for identifying adverse drug events, and an f-measure of 84 percent in patient report extraction. Their proposed methods significantly outperformed prior work in extracting patient reports of adverse drug events in health social media.
Slip detection and prediction in human walking using only wearable inertial measurement units (IMUs)
(2015)

Slip and fall is one of the major causes for human injuries for elders and professional workers. Real-time detection and prediction of the foot slip is critical for developing effective assistive and rehabilitation devices to prevent falls and train balance disorder patients. This paper presents a novel real-time slip detection and prediction scheme with wearable inertial measurement units (IMUs). The slip-detection algorithm is built on a new dynamic model for bipedal walking with slips. An extended Kalman filter is designed to reliably predict the foot slip displacement using the wearable IMU measurements and kinematic constraints. The proposed slip detection and prediction scheme has been demonstrated by extensive experiments.